In this section, we compare the quality of the generated molecules and the model performance with three baseline models, MORLD [1]}, REINVENT [2]}, and HierVAE [3]}. Our model, FREED(PE), is our fragment-based generation method coupled with SAC and PER(PE). MORLD and REINVENT are the models utilized for docking score optimization tasks in previous works [1]}, [5]}, [6]}. HierVAE is a strong non-RL fragment-based molecular generative model. Since HierVAE is a distributional learning method, we train HierVAE in two schemes - `one-time' training on the known active molecules (HierVAE) and `active learning (AL)' training where we train the model once on the known actives and twice on the top-scoring molecules from the generated molecules (HierVAE(AL)). In both schemes, we initialized the models with the model pre-trained on ChEMBL.
We trained the models for three carefully chosen protein targets fa7, parp1, and 5ht1b. The choice of protein targets and the specifics of experimental settings are described in Appendix REF .
For the experiments in this section, we use the small fragment library that includes 66 pharmacochemically acceptable fragments.
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